Safety Recommendations in Shotokan Karate
Bibliographic record
Abstract
OBJECTIVE: To study risk factors for injury in karate and to establish safety recommendations. DESIGN: Cross-sectional survey of karate injuries. SETTING: Shotokan karate clubs in Boston, Massachusetts, Dallas, Texas, and Winnipeg, Manitoba, Canada. PARTICIPANTS: All athletes training at each club received surveys. A total of 114 surveys were analyzed (74% response rate). MAIN OUTCOME MEASURES: Presence of injuries (requiring any time off from practice), major injuries (requiring at least 7 days off), and multiple injuries (3 or more injuries). RESULTS: No statistically significant differences were found with respect to sex. For all outcomes, karateka younger than 18 years of age had fewer injuries. The number of karateka with injuries and with multiple injuries increased with belt rank until brown belt, then reached a plateau. Brown and black belts had a greater frequency of major injuries than the lower ranks. Training more than 3 hours per week correlated with an increase in injuries, major injuries, and multiple injuries. CONCLUSION: Shotokan karate appears to be a safe sport, especially for those younger than 18 years of age. Risk of injury increases significantly when younger karateka of any rank or older karateka of lower ranks train more than 3 hours per week; therefore, to reduce the risk of injury to less than 50%, weekly training should be limited to a maximum of 3 hours in these groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".